Now that you’ve created your account, you’re ready to deploy your first Pod:
Deploy via the console
Deploy via the CLI
Use this path to launch a Pod from the console. Configure your workload, choose a region and GPU, review the pricing, and deploy.Runpod is rolling out an updated deployment flow through early access. Use the tabs below to follow the version that matches what you see in the console.
Early access flow
Legacy flow
These instructions describe the early access version of the deployment flow. If you don’t see this flow, follow the Legacy flow tab, or enable the new flow from Account → Early access.
1
Configure your workload
In the Workload panel on the left:
Template: select the container image your Pod will run. Use the search box to find a template, or click Explore all to browse. The selected template and its image appear below the search bar. Click Set overrides to customize environment variables, exposed ports, or container start commands without editing the template itself.
Pod name: a name is auto-generated. You can replace it with any name you prefer.
Options: when you select an official Runpod template, two extra options appear. These options are not shown for community templates.
Start Jupyter notebook: launches a Jupyter server when the Pod starts, accessible from the console. Enabled by default.
SSH terminal access: enables SSH into the Pod. If enabled, paste your SSH public key in the field that appears. Learn how to create an SSH key.
2
Choose a region
Set the Region for your Pod. It’s set to Any region by default. Click to restrict deployment to a specific geographic region.
3
Select a GPU
In the Compute panel, choose a GPU from one of four tabs:
Available: shows only GPUs with capacity right now.
Recommended: shows GPUs recommended by Runpod and by the template maintainer.
All: shows all GPUs, including those that are out of capacity and those marked incompatible by the template.
Recent: shows all GPUs you’ve deployed in the last 7 days.
Use the search box, Network volume filter, Filter button, or Sort by dropdown to narrow the list. Click Compare GPUs to ask the Runpod assistant for a comparison of the selected GPUs.The Network volume filter narrows the GPU list to compatible GPUs by filtering for GPUs that are in the same data center as the selected network volume. Select any network volume, and GPU availability updates to match it. GPUs that aren’t compatible with the selected volume move to the incompatible section of the All tab.If the GPU you want shows Out of capacity, you can still select it and deploy it once capacity frees up. Selecting an out-of-capacity GPU changes the final Deploy step: instead of deploying right away, you can subscribe to deploy when the GPU becomes available.
4
Configure storage
Pods offer two kinds of storage.Container diskThis is the container’s primary storage, and it’s wiped whenever the Pod is stopped.Persistent storagePersistent storage keeps your data across stops and restarts, and you can mount it at any location. It’s mounted at /workspace by default, but you can change this with a template override, or the template itself can set a different default. It comes in two types, and you can attach one or the other but not both:
Volume disk: a disk attached directly to your Pod that keeps its data across stops and restarts but is deleted when the Pod is terminated.
Network volume: permanent storage that exists independently of any Pod, so you can attach the same volume to different Pods over time.
If you did select a network volume in the Compute step, the persistent storage selection in this step is locked to that network volume.GPUs that aren’t compatible with any network volume have volume disk selected automatically, and you can’t select a network volume for them.For a full comparison, see Storage options.
5
Review the pricing summary
The Summary panel on the right shows:
The selected template and GPU.
Total cost per hour (billed per millisecond).
A breakdown of GPU cost, container disk cost, persistent storage cost, and stopped cost.
Verify the configuration looks correct before deploying.
6
Deploy
Deploy available Pod
Deploy when available
Click Deploy Pod to launch your Pod.Your Pod appears under Pods in the left sidebar. It may take a moment to reach the running state while the container image is pulled.
If you haven’t set up payments yet, you’ll be prompted to add a payment method and purchase credits for your account.
If you selected an out-of-capacity GPU, the Summary panel shows Instance not available and a Deploy when available button in place of Deploy Pod. Runpod queues your Pod and deploys it automatically as soon as that GPU frees up within the time window you choose.
1
Click Deploy when available
In the right panel, click the Deploy when available button to open the subscription modal.
2
Set your subscription options
In the Deploy when available modal:
Notifications: choose how you want to be notified when your Pod deploys:
Email: sends a notification to one of your account’s email addresses. Select which address to use. If you’re a member of a team, you can also select the team’s email address.
In-console notification: shows an alert inside the Runpod console.
Both are enabled by default.
Subscription window: set the time range during which Runpod monitors for availability and auto-deploys your Pod:
The default window is 24 hours from now.
Click the date range to adjust the start and end time.
Check Use advanced scheduler to fine-tune the hours when Runpod can deploy your request, rather than monitoring continuously across the entire window.
3
Subscribe
Click Subscribe to confirm. Runpod monitors for availability and deploys your Pod automatically when the selected GPU becomes free within your subscription window.You’ll receive a notification (via email or in-console, depending on your settings) when the Pod has been deployed.
If you don’t have enough funds available when the GPU becomes available, your deploy when available subscription fails and no Pod is deployed. To retry, you’ll need to subscribe again.
If the GPU doesn’t become available within your subscription window, your subscription expires and no Pod is deployed. You can create a new subscription at any time.
1
Select a GPU
Select a GPU from the list of graphics cards.
2
Select a template
Select the template you want to use. To customize environment variables, exposed ports, or container start commands, click Edit to set template overrides.
3
Deploy
Click Deploy On-Demand to deploy and start your Pod. You’ll be redirected back to the Pods page after a few seconds.
If you haven’t set up payments yet, you’ll be prompted to add a payment method and purchase credits for your account.
First, install the Runpod CLI on your local machine and configure it with your API key:
# Install runpodctlbash <(wget -qO- cli.runpod.io)# Configure your API keyrunpodctl config --apiKey "your-api-key"
To avoid incurring unnecessary charges, clean up your Pod resources.
Terminating a Pod permanently deletes all data that isn’t stored in a . Be sure that you’ve saved any data you might need to access again.
Web
Terminal
To stop your Pod:
Return to the Pods page and click your running Pod.
Click the Stop button (pause icon) to stop your Pod.
Click Stop Pod in the modal that opens to confirm.
You’ll still be charged a small amount for storage on stopped Pods ($0.20 per GB per month). If you don’t need to retain any data on your Pod, you should terminate it completely.To terminate your Pod:
Click the Terminate button (trash icon).
Click Terminate Pod to confirm.
Stop your Pod:
runpodctl pod stop $RUNPOD_POD_ID
You’ll still be charged a small amount for storage on stopped Pods ($0.20 per GB per month). If you don’t need to retain any data on your Pod, terminate it completely:
